ACCD detects coordinated online harm campaigns by combining adaptive causal temporal scoring, semi-supervised account classification, and stability validation. On a filtered Twitter IRA task, it improves account-level coordination screening over correlation and fixed-parameter CCM baselines, while reducing manual labeling and runtime.

Coordinated inauthentic behavior (CIB) threatens the integrity of public conversation, yet existing detectors remain unreliable in practice. Most systems measure correlation between account activities, which conflates genuine influence with shared reactions to trending topics. They also apply fixed detection parameters and require extensive manual labeling, so configurations tuned on one campaign rarely transfer to another. We present ACCD (Adaptive Causal Coordination Detection), a framework that treats this problem as memory-guided causal inference. ACCD screens account pairs in three stages that each narrow the candidate set: it retrieves convergent-cross-mapping parameters from a context-indexed memory of past performance to estimate causal influence, concentrates annotation on uncertain accounts through active learning in a semi-supervised classifier whose labels filter the surviving pairs, and validates the remainder with estimators and thresholds chosen from prior experience. On the Twitter Internet Research Agency (IRA) dataset, ACCD improves pair-level F1 from 75.8% to 87.3% over a fixed-parameter causal baseline, reduces manual labeling by 68.3%, and cuts end-to-end detection time by 2.5×. On Reddit coordination traces it reaches 84.7% F1. On synthetic data with known causal structure, it recovers true influence edges at 0.934 area under the ROC curve (AUC) against 0.708 for correlation-based detection, and the gap widens to 0.861 against 0.586 once strong confounding is introduced. These results suggest that adapting causal configurations to detection context provides a practical and efficient screening tool for protecting online public discourse.